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Efficient Development of User-Defined Image Recognition Systems

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Computer Vision - ACCV 2012 Workshops (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7728))

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Abstract

Development processes for building image recognition systems are highly specialized and require expensive expert knowledge. Despite some effort in developing generic image recognition systems, use of computer vision technology is still restricted to experts. We propose a flexible image recognition system (FOREST), which requires no prior knowledge about the recognition task and allows non-expert users to build custom image recognition systems, which solve a specific recognition task defined by the user. It provides a simple-to-use graphical interface which guides users through a simple development process for building a custom recognition system. FOREST integrates a variety of feature descriptors which are combined in a classifier using a boosting approach to provide a flexible and adaptable recognition framework. The evaluation shows, that image recognition systems developed with this framework are capable of achieving high recognition rates.

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Moehrmann, J., Heidemann, G. (2013). Efficient Development of User-Defined Image Recognition Systems. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37410-4_21

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  • DOI: https://doi.org/10.1007/978-3-642-37410-4_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37409-8

  • Online ISBN: 978-3-642-37410-4

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